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  4. Automated detection of palatal deformations using deep learning on endoscopic images
 
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Automated detection of palatal deformations using deep learning on endoscopic images

Citation Link: https://doi.org/10.15480/882.13350
Publikationstyp
Journal Article
Date Issued
2024-09-01
Sprache
English
Author(s)
Schild, Leona
Zang, Jana  
Flügel, Till  
Weiss, Deike
Schlaefer, Alexander  
Medizintechnische und Intelligente Systeme E-1  
Latus, Sarah  orcid-logo
Medizintechnische und Intelligente Systeme E-1  
TORE-DOI
10.15480/882.13350
TORE-URI
https://hdl.handle.net/11420/49338
Journal
Current directions in biomedical engineering  
Volume
10
Issue
1
Start Page
65
End Page
68
Citation
Current Directions in Biomedical Engineering 10 (1): 65-68 (2024)
Publisher DOI
10.1515/cdbme-2024-0117
Scopus ID
2-s2.0-85204934291
Publisher
De Gruyter
A deformation of the hard palate can occur in spinal muscular atrophy and leads to problems with feeding and swallowing in early childhood. An objective analysis of the palatal changes is therefore desirable for early treatment initiation. In this study, we investigate a deep learning approach to automatically detect deformation in endoscopic images which were collected in a prospective in-vivo study on 33 infants. Ratings of five different experts were used to quantify the deformation and to train our network. We investigate different network architectures and data set splits and achieve classification performances of up to 0.85 ± 0.05 when distinguishing between normal and deformation using the EfficientNet architecture. This combination of endoscopic imaging and deep learning offers a first approach for the objective assessment of palatal changes.
Subjects
convolutional neural networks
endoscopic analysis
palatal deformations
spinal muscular atrophy
MLE@TUHH
DDC Class
610: Medicine, Health
Lizenz
https://creativecommons.org/licenses/by/4.0/
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